Repeat or chronic?: examining police data accuracy across the ‘history’ classifications of missing person cases

Abstract

ABSTRACT Going missing multiple times, which can render missing person cases as ‘repeat’ or ‘chronic,’ has been widely regarded as an issue that generates police resource strains. Within existing missing persons literature, the ambiguities surrounding what constitutes a missing individual as ‘repeat’ or ‘chronic’ have been discussed. The main issues being that there is no proper definition for, nor is there any research on, how many times an individual is reported missing before they are assigned these classifications. These arbitrarily designated categories can have implications for police risk assessment and response. This issue can also lead to discrepancies in police missing persons data quality, inaccurate figures on the types of cases, and challenges with developing police practices and policies. This article aims to examine the ‘history’ case classifications of persons reported missing to the police, which, among other factors, serve as indicators to make judgements for risk assessment purposes. To do so, we use multinomial logistic regression to predict case classification types based on the number of previous missing reports generated for each record. Results reveal clear distinctions in how cases should be classified. As such, we offer insights into potential standardised measures for categorising a missing person case as ‘repeat’ and ‘habitual/chronic.’ We then discuss the broader implications of the disconnection between missing person classifications and their use, and how the lack of definitions undermine the utility of categories for risk assessment in police missing persons work.